This is in continuation of file:All_type_cases.Rmd

1 options & settings

1.1 chunk options

CSS for scrollable output & Header colors

Turning scientific / Exponential numbers off

options(scipen = 999)

1.2 Loading libs

library(tidyverse)
library(ggthemes)

library(lubridate)
library(covid19.analytics)
library(data.table)
library(plotly)
library(gghighlight)
library(zoo)   # this is for moving averages
# library(ggThemeAssist) 
library(glue)
library(scales)
library(ggtext)
library(patchwork)
library(ggforce)

1.3 Creating & setting custom theme

theme_viny_bright <- function(){
  
  library(ggthemes)
  
  ggthemes::theme_fivethirtyeight() %+replace%
  
  theme(
    axis.title = element_text(),
    axis.text = element_text(size = 13),
    legend.text = element_text(size = 10),
    panel.background = element_rect(fill = "white"),
    plot.background = element_rect(fill = "white"),
    strip.background = element_blank(),
    legend.background = element_rect(fill = NA),
    legend.key = element_rect(fill = NA),
    plot.title = element_text(hjust = 0.5, size = 14, face = "plain",
                              family = "serif"),
    plot.subtitle = element_text(hjust = 0.5,face = "plain", family = "serif", size = 9),
    plot.caption = element_text(hjust = 1, colour = "maroon", size = 8)
      )
  }

theme_set(theme_viny_bright())

2 Dual axis plot

2.1 Lineplt: Dailycases Vs Dailydeaths

2.1.1 Data Prep.

grouped_cases_df %>% head()

scaleFactor = max(grouped_cases_df %>% 
                    filter(Country.Region == "India") %>% 
                    pull(Daily_cases)) / 
              max(grouped_cases_df %>% 
                    filter(Country.Region == "India") %>% 
                    pull(Daily_deaths))

scaleFactor

2.1.2 Basic dual axis plot

ggplot(data = (grouped_cases_df %>% filter(Country.Region == "India")),
       aes(x = Date)) +
  geom_line(aes(y = Daily_cases), col = "blue") +
  geom_line(aes(y = Daily_deaths * scaleFactor), col = "red") +
  scale_y_continuous(name = "Daily Cases", sec.axis = sec_axis(~./scaleFactor, name = "Daily Deaths")) +
  
  theme(
    axis.title.y.left=element_text(color="blue"),
    axis.text.y.left=element_text(color="blue"),
    axis.title.y.right=element_text(color="red"),
    axis.text.y.right=element_text(color="red")
  )

2.1.3 Function1 dual axis plot

this function supports passing of Country.Region names without using quotes but it creates problems for countries that have space in name.

fn_Comapare_confrm_to_deaths <- function(Country_selected = India) {
  Country_selected_enquo = enquo(Country_selected)
  
  daily_col = "#32a4ba"
  death_col = "#f08080"

grouped_cases_df %>%
          filter(Country.Region == quo_name(Country_selected_enquo)) %>%

ggplot(aes(x = Date)) +
  geom_line(aes(y = Daily_cases), col = daily_col, size = 0.8) +
  geom_line(aes(y = Daily_deaths * scaleFactor), col = death_col, size = 0.8, 
            size = 0.8, alpha = 0.8) +
  scale_y_continuous(name = "Daily Cases", sec.axis = sec_axis(~./scaleFactor, name = "Daily Deaths"),
                     labels = scales::comma_format()) +
  scale_x_date(date_breaks = "1 month", date_labels = "%b") +
  
  theme_excel_new() +

  theme(
    axis.title.y.left=element_text(color=daily_col),
    axis.text.y.left=element_text(color=daily_col),
    axis.title.y.right=element_text(color=death_col),
    axis.text.y.right=element_text(color=death_col),
    plot.title = element_text(face = "plain", family = "serif", size = 14)
  ) + 
  
  theme(plot.title = element_markdown() ) +
  
  labs(title = glue("<i>{quo_name(enquo(Country_selected))}</i>: Daily confirmed & death cases"),
       caption = "created by ViSa (SciArt!!)") +
  coord_fixed(ratio = .0028)
}

fn_Comapare_confrm_to_deaths(Germany)

2.1.4 Function2 dual axis plot

this function requires passing of Country.Region names within quotes only support country names that have space in it.

fn_Comapare_confrm_to_deaths <- function(Country_selected = "India") {
  # Country_selected_enquo = enquo(Country_selected)
  
  daily_col = "#32a4ba"
  death_col = "#f08080"

grouped_cases_df %>%
          filter(Country.Region == Country_selected) %>%

ggplot(aes(x = Date)) +
  geom_line(aes(y = Daily_cases), col = daily_col, size = 0.8) +
  geom_line(aes(y = Daily_deaths * scaleFactor), col = death_col, 
            size = 0.8, alpha = 0.8) +
  scale_y_continuous(name = "Daily Cases", sec.axis = sec_axis(~./scaleFactor, name = "Daily Deaths"),
                     labels = scales::comma_format()) +
  scale_x_date(date_breaks = "1 month", date_labels = "%b") +
  
  theme_excel_new() +
  theme(
    axis.title.y.left=element_text(color=daily_col),
    axis.text.y.left=element_text(color=daily_col),
    axis.title.y.right=element_text(color=death_col),
    axis.text.y.right=element_text(color=death_col),
    plot.title = element_text(face = "plain", family = "serif", size = 14)
  ) + 
  theme(plot.title = element_markdown()) +
  labs(title = glue("<i>{Country_selected}</i>: Daily confirmed & death cases"),
       caption = "created by ViSa (SciArt!!)") +
    
  coord_fixed(ratio = .0028)
  # coord_fixed(ratio = .008) 
}

plt1 <- fn_Comapare_confrm_to_deaths("United Kingdom")
plt1

2.1.5 plotly

ggplotly(fn_Comapare_confrm_to_deaths("United Kingdom"))

2.1.6 CFR level plt

fn_CFR <- function(Country_selected = "India") {
  grouped_cases_df %>% 
    filter(Country.Region == Country_selected) %>% 
    
    ggplot(aes(x = Date, y = Case_Fatality_Ratio, col = CFR_level, group = 1)) +
    geom_line(size = 0.8) +
    scale_y_continuous(labels = label_dollar(prefix = "", suffix = "%")) +
    scale_x_date(date_breaks = "1 month", date_labels = "%b") +
    theme_excel_new() +
    theme(legend.position = "bottom",
          plot.title = element_text(face = "plain", family = "serif", size = 14)) +
    
    theme(plot.title = element_markdown()) +
    labs(title = glue("<i>{Country_selected}</i>: Case Fatality Ratio(%) & level"),
         y = "CFR(%)",
         caption = "created by ViSa (SciArt!!)") +
    # ylab(label = "CFR (%)")
      
    coord_fixed(ratio = 4) 
}
  
plt2 <- fn_CFR("United Kingdom")
plt2

2.1.7 Patched Plots

plt1 / plt2

ggsave(filename = "UK_Dailycases.png", dpi = 300)

2.2 Colplt: Dailycases Vs Dailydeaths

2.2.1 Basic dual axis plot

ggplot(data = (grouped_cases_df %>% filter(Country.Region == "India")),
       aes(x = Date)) +
  geom_col(aes(y = Daily_cases), col = "blue") +
  geom_line(aes(y = Daily_deaths * scaleFactor), col = "red") +
  scale_y_continuous(name = "Daily Cases", sec.axis = sec_axis(~./scaleFactor, name = "Daily Deaths")) +
  
  theme(
    axis.title.y.left=element_text(color="blue"),
    axis.text.y.left=element_text(color="blue"),
    axis.title.y.right=element_text(color="red"),
    axis.text.y.right=element_text(color="red")
  )

2.2.2 Function1 dual axis plot

this function supports passing of Country.Region names without using quotes but it creates problems for countries that have space in name.

fn_Comapare_confrm_to_deaths <- function(Country_selected = India) {
  Country_selected_enquo = enquo(Country_selected)
  
  daily_col = "#32a4ba"
  death_col = "#f08080"

grouped_cases_df %>%
          filter(Country.Region == quo_name(Country_selected_enquo)) %>%

ggplot(aes(x = Date)) +
  geom_col(aes(y = Daily_cases), fill = daily_col, size = 0.8) +
  geom_line(aes(y = Daily_deaths * scaleFactor), col = death_col, size = 0.8, 
            size = 0.8, alpha = 0.8) +
  scale_y_continuous(name = "Daily Cases", sec.axis = sec_axis(~./scaleFactor, name = "Daily Deaths"),
                     labels = scales::comma_format()) +
  scale_x_date(date_breaks = "1 month", date_labels = "%b") +
  
  theme_excel_new() +

  theme(
    axis.title.y.left=element_text(color=daily_col),
    axis.text.y.left=element_text(color=daily_col),
    axis.title.y.right=element_text(color=death_col),
    axis.text.y.right=element_text(color=death_col),
    plot.title = element_text(face = "plain", family = "serif", size = 14)
  ) + 
  
  theme(plot.title = element_markdown() ) +
  
  labs(title = glue("<i>{quo_name(enquo(Country_selected))}</i>: Daily confirmed & death cases"),
       caption = "created by ViSa (SciArt!!)") +
  coord_fixed(ratio = .0028)
}

fn_Comapare_confrm_to_deaths(Germany)

2.2.3 Function2 dual axis plot

this function requires passing of Country.Region names within quotes only support country names that have space in it.

fn_Comapare_confrm_to_deaths <- function(Country_selected = "India") {
  # Country_selected_enquo = enquo(Country_selected)
  
  daily_col = "#32a4ba"
  death_col = "#f08080"

grouped_cases_df %>%
          filter(Country.Region == Country_selected) %>%

ggplot(aes(x = Date)) +
  geom_col(aes(y = Daily_cases), fill = daily_col, size = 0.8) +
  geom_line(aes(y = Daily_deaths * scaleFactor), col = death_col, 
            size = 0.8, alpha = 0.8) +
  scale_y_continuous(name = "Daily Cases", sec.axis = sec_axis(~./scaleFactor, name = "Daily Deaths"),
                     labels = scales::comma_format()) +
  scale_x_date(date_breaks = "1 month", date_labels = "%b") +
  
  theme_excel_new() +
  theme(
    axis.title.y.left=element_text(color=daily_col),
    axis.text.y.left=element_text(color=daily_col),
    axis.title.y.right=element_text(color=death_col),
    axis.text.y.right=element_text(color=death_col),
    plot.title = element_text(face = "plain", family = "serif", size = 14)
  ) + 
  theme(plot.title = element_markdown()) +
  labs(title = glue("<i>{Country_selected}</i>: Daily confirmed & death cases"),
       caption = "created by ViSa (SciArt!!)") +
    
  coord_fixed(ratio = .0028) 
}

plt1 <- fn_Comapare_confrm_to_deaths("United Kingdom")
plt1

2.2.4 plotly

ggplotly(fn_Comapare_confrm_to_deaths("United Kingdom"))

2.2.5 CFR level plt

fn_CFR <- function(Country_selected = "India") {
  grouped_cases_df %>% 
    filter(Country.Region == Country_selected) %>% 
    
    ggplot(aes(x = Date, y = Case_Fatality_Ratio, col = CFR_level, group = 1)) +
    geom_line(size = 0.8) +
    scale_y_continuous(labels = label_dollar(prefix = "", suffix = "%")) +
    scale_x_date(date_breaks = "1 month", date_labels = "%b") +
    theme_excel_new() +
    theme(legend.position = "bottom",
          plot.title = element_text(face = "plain", family = "serif", size = 14)) +
    
    theme(plot.title = element_markdown()) +
    labs(title = glue("<i>{Country_selected}</i>: Case Fatality Ratio(%) & level"),
         y = "CFR(%)",
         caption = "created by ViSa (SciArt!!)") +
    # ylab(label = "CFR (%)")
      
    coord_fixed(ratio = 4) 
}
  
plt2 <- fn_CFR("United Kingdom")
plt2

2.2.6 Patched Plots

plt1 / plt2

3 Latest Week plots

3.1 Top 10 in 7 days

3.1.1 facet by Date

df_stack %>% 
  filter(str_starts(df_stack$Cases_type, pattern = "Daily_"),
                    Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  
  ggplot(aes(x = Cases_type, y = Country.Region)) +
  geom_point(shape = 21, aes(size = Cases_size, color = Cases_type), fill="#f8f2e4", stroke=3) +
  # theme_minimal() +
  theme_wsj() +
  theme(axis.text.x = element_text(angle = 90),
        axis.text = element_text(size = 8),
        legend.position = "none",
        plot.title = element_text(face = "bold", family = "serif", size = 20),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 12),
        plot.caption = element_text(face = "plain", family = "serif", size = 9)) +
  scale_color_tableau(palette = "Tableau 10") +
  # scale_color_wsj(palette = "colors6") +
  
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries for all type of Daily cases",
       subtitle = "For Latest 7 days (Size based on each category count for same date)",
       caption = "created by ViSa (SciArt!!)") +
  coord_cartesian(clip = "off")

3.1.2 +ggtext colr axis

ref: https://wilkelab.org/ggtext/

solution from: https://stackoverflow.com/questions/65652600/how-to-use-ggtext-to-color-axis-data-labels-in-r/65652801#65652801

color = c(Daily_cases = "#2596be", Daily_deaths = "#f28e2b", Daily_recovered = "#e15759")

df_stack %>% 
  filter(str_starts(df_stack$Cases_type, pattern = "Daily_"),
                    Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  # mutate(Cases_type = glue("<i style='color:{color}'>{Cases_type}</i>")) %>% 
  mutate(Cases_type = glue("<i style='color: {color[Cases_type]}'>{Cases_type}</i>")) %>% 

  ggplot(aes(x = Cases_type, y = Country.Region)) +
  geom_point(shape = 21, aes(size = Cases_size, color = Cases_type), fill="#f8f2e4", stroke=3) +
  #theme_minimal() +
  theme_wsj() +
  theme(axis.text.x = element_text(angle = 90),
        axis.text = element_text(size = 8),
        legend.position = "none",
        plot.title = element_text(face = "bold", family = "serif", size = 20),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 12),
        plot.caption = element_text(face = "plain", family = "serif", size = 9)) +
  theme(axis.text.x = element_markdown()) +
  scale_color_manual(values = c("#2596be", "#e15759", "#f28e2b")) +
  # scale_color_tableau(palette = "Tableau 10") +
  # scale_color_wsj(palette = "colors6") +
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries for all type of Daily cases",
       subtitle = "For Latest 7 days separated by each date",
       caption = "created by ViSa (SciArt!!)") +
  coord_cartesian(clip = "off")

3.1.3 working solution

color = c(Daily_cases = "#2596be", Daily_deaths = "#e15759", Daily_recovered = "#f28e2b")

read.csv("https://raw.githubusercontent.com/johnsnow09/covid19-df_stack-code/main/df_stack_limited.csv") %>%
  mutate(Cases_type = glue("<i style='color: {color[Cases_type]}'>{Cases_type}</i>")) %>% 

  ggplot(aes(x = Cases_type, y = Country.Region)) +
  geom_point(shape = 21, aes(size = Cases_size, color = Cases_type), fill="#f8f2e4", stroke=3) +
  #theme_minimal() +
  theme_wsj() +
  theme(axis.text.x = element_text(angle = 90),
        axis.text = element_text(size = 8),
        legend.position = "none",
        plot.title = element_text(face = "bold", family = "serif", size = 20),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 12),
        plot.caption = element_text(face = "plain", family = "serif", size = 9)) +
  theme(axis.text.x = element_markdown()) +
  scale_color_manual(values = c("#2596be", "#e15759", "#f28e2b")) +
  # scale_color_tableau(palette = "Tableau 10") +
  # scale_color_wsj(palette = "colors6") +
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries for all type of Daily cases",
       subtitle = "For Latest 7 days (Size based on each category count for same date)",
       caption = "created by ViSa (SciArt!!)") +
  coord_cartesian(clip = "off")

3.1.4 facet by Cases_type

df_stack %>% 
  filter(str_starts(df_stack$Cases_type, pattern = "Daily_"),
         Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  
  ggplot(aes(x = Date, y = Country.Region)) +
  geom_point(shape = 21, aes(size = Cases_size, color = Date), fill="#f8f2e4", stroke=3) +
  # theme_minimal() +
  theme_wsj() +
  theme(axis.text.x = element_text(angle = 90),
        axis.text = element_text(size = 8),
        legend.position = "none",
        plot.title = element_text(face = "bold", family = "serif", size = 20),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 12),
        plot.caption = element_text(face = "plain", family = "serif", size = 9)) +
  # scale_color_tableau(palette = "Tableau 20") +
  
  facet_wrap(~Cases_type) +
  labs(title = "Top 5 Countries for all type of Daily cases",
       subtitle = "For Latest 7 days separated by Cases Type",
       caption = "created by ViSa (SciArt!!)") +
  coord_cartesian(clip = "off")

3.2 Top5 Barplots of Cases

3.2.1 +Barplt (Daily_cases)

df_stack %>% 
  filter(Cases_type == "Daily_cases",
         Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Cases_count, max)) %>% 
  
  ggplot(aes(x = Cases_count, y = Country.Region)) +
  geom_col(aes(fill = Country.Region)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none",
        plot.title = element_text(face = "plain", family = "serif", size = 14),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 9)) +
  scale_fill_tableau(palette = "Tableau 20") +
  scale_x_continuous(labels = scales::unit_format(scale = 1e-3, unit = "k")) +
  
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries by Daily cases",
       subtitle = "For Latest 7 days separated by Dates",
       caption = "created by ViSa (SciArt!!)",
       x = "Daily Cases count",
       y = "") +
  coord_cartesian(clip = "off")

3.2.2 +Barplt (Daily_recovered)

df_stack %>% 
  filter(Cases_type == "Daily_recovered",
         Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Cases_count, max)) %>% 
  
  ggplot(aes(x = Cases_count, y = Country.Region)) +
  geom_col(aes(fill = Country.Region)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none",
        plot.title = element_text(face = "plain", family = "serif", size = 14),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 9),
        plot.background = element_rect(colour = "black", size = 1) ) +
  scale_fill_tableau(palette = "Tableau 20") +
  scale_x_continuous(labels = scales::unit_format(scale = 1e-3, unit = "k")) +
  
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries by Daily Recovered cases",
       subtitle = "For Latest 7 days separated by Dates",
       caption = "created by ViSa (SciArt!!)",
       x = "Daily Recovered Cases count",
       y = "") +
  coord_cartesian(clip = "off")

3.2.3 +Barplt (Daily_deaths)

df_stack %>% 
  filter(Cases_type == "Daily_deaths",
         Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Cases_count, max)) %>% 
  
  ggplot(aes(x = Cases_count, y = Country.Region)) +
  geom_col(aes(fill = Country.Region)) +
  # theme_minimal() +
  theme_clean() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none",
        plot.title = element_text(face = "plain", family = "serif", size = 14),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 9)) +
  scale_fill_tableau(palette = "Tableau 20") +
  scale_x_continuous(labels = scales::unit_format(scale = 1e-3, unit = "k")) +
  
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries by Daily Deaths",
       subtitle = "For Latest 7 days",
       caption = "created by ViSa (SciArt!!)",
       x = "Daily Death Cases count",
       y = "") +
  coord_cartesian(clip = "off")

3.2.4 plotly +subtitle

ggplotly(df_stack %>% 
  filter(Cases_type == "Daily_deaths",
         Date > (max(Date, na.rm = TRUE) - 8) & Date < max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count)) %>%
  group_by(Date, Cases_type) %>% 
  slice_max(order_by = Cases_count, n = 5) %>% 
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Cases_count, max)) %>% 
  
  ggplot(aes(x = Cases_count, y = Country.Region)) +
  geom_col(aes(fill = Country.Region)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none",
        plot.title = element_text(face = "plain", family = "serif", size = 14),
        plot.subtitle = element_text(face = "plain", family = "serif", size = 9)) +
  scale_fill_tableau(palette = "Tableau 20") +
  scale_x_continuous(labels = scales::unit_format(scale = 1e-3, unit = "k")) +
  
  facet_wrap(~Date) +
  labs(title = "Top 5 Countries of Daily Deaths cases",
       subtitle = "For Latest 7 days separated by Dates",
       caption = "created by ViSa (SciArt!!)",
       x = "Daily Death Cases count",
       y = "") +
  coord_cartesian(clip = "off") 
) %>%
  layout(title = list(text = paste0('Top 5 Countries of Daily Deaths cases',
                                    '<br>',
                                    '<sup>',
                                    'For Latest 7 days separated by Dates',
                                    '</sup>')))

4 Cases Distribution by continent

4.1 Latest Confirmed Cases

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)#,
         # Confirmed < 5000000
         ) %>% 
  
  ggplot(aes(x = continent, y = log(Confirmed), label = Country.Region)) +
  geom_jitter(size = 1.8, width = 0.35, aes(shape = CFR_level, color = CFR_level)) +
  geom_violin(alpha = .5, color = 'grey50', draw_quantiles = c(.25, .5, .75)) +
  labs(title = "Latest Confirmend cases across continents",
       subtitle = "Colored by CFR severity level",
       caption = "created by ViSa (SciArt!!)")

4.2 +plotly

ggplotly(grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)#,
         # Confirmed < 5000000
         ) %>% 
  
  ggplot(aes(x = continent, y = log(Confirmed), label = Country.Region)) +
  geom_jitter(size = 1.8, width = 0.35, aes(shape = CFR_level, color = CFR_level)) +
  geom_violin(alpha = .5, color = 'grey50', draw_quantiles = c(.25, .5, .75)) +
  labs(title = "Latest Confirmend cases across continents",
       subtitle = "Colored by CFR severity level",
       caption = "created by ViSa (SciArt!!)")
)

4.3 Latest Active Cases

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)#,
         # Active < 5000000
         ) %>% 
  
  ggplot(aes(x = continent, y = log(Active), label = Country.Region)) +
  geom_jitter(size = 1.8, width = 0.35, aes(shape = CFR_level, color = CFR_level)) +
  geom_violin(alpha = .5, color = 'grey50', draw_quantiles = c(.25, .5, .75)) +
  labs(title = "Latest Active cases across continents",
       subtitle = "Colored by CFR severity level",
       caption = "created by ViSa (SciArt!!)")

4.4 +plotly tooltip

ggplotly(grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)#,
         # Active < 5000000
         ) %>% 
  
  ggplot(aes(x = continent, y = log(Active), label = Country.Region)) +
  geom_jitter(size = 1.8, width = 0.35, aes(shape = CFR_level, color = CFR_level)) +
  geom_violin(alpha = .5, color = 'grey50', draw_quantiles = c(.25, .5, .75)) +
  labs(title = "Latest Active cases across continents",
       subtitle = "Colored by CFR severity level",
       caption = "created by ViSa (SciArt!!)")

  , tooltip = c("log(Active)", "continent", "label"))

4.5 ggforce/geom_sina

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)#,
         # Active < 5000000
         ) %>% 
  
  ggplot(aes(x = continent, y = log(Active), label = Country.Region)) +
  geom_violin(alpha = .5, color = 'grey50', draw_quantiles = c(.25, .5, .75)) +
  ggforce::geom_sina(alpha = .7, size = 1.8, width = 0.35, aes(shape = CFR_level, color = CFR_level)) +
  # geom_jitter() +
  labs(title = "Latest Active cases across continents",
       subtitle = "Colored by CFR severity level",
       caption = "created by ViSa (SciArt!!)")

4.6 +plotly

ggplotly(grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)#,
         # Active < 5000000
         ) %>% 
  
  ggplot(aes(x = continent, y = log(Active), label = Country.Region)) +
  geom_violin(alpha = .5, color = 'grey50', draw_quantiles = c(.25, .5, .75)) +
  ggforce::geom_sina(size = 1.8, width = 0.35, aes(shape = CFR_level, color = CFR_level)) +
  # geom_jitter() +
  
  labs(title = "Latest Active cases across continents",
       subtitle = "Colored by CFR severity level",
       caption = "created by ViSa (SciArt!!)")
  
  , tooltip = c("log(Active)", "continent", "label"))